Time Frequency Distribution for Vibration Signal Analysis with Application to Turbo-generator Fault Diagnosis
With the growing demand for improving reliability and performance of turbo-generator set as an important power supply in modern electric industry, the vibration signal processing and fault diagnosis techniques have been developed for dynamic feature extraction and pattern recognition. A new approach using wavelet transform and fuzzy network modeling is presented for vibration fault diagnosis of turbo-generator set. The wavelet transform decomposes the vibration signal in time domain into a two-dimensional function in time-scale plane, which analyzes the low-frequency component of signal with a wide duration function and analyzes conversely high-frequency component with short-duration function. The wavelet fuzzy method can generate dilation and translation parameters, and the network output vector is a linear combination of fuzzy wavelet basis functions. The approximation procedure can achieve better approximation of accuracy order than normal neural network and fuzzy system. The experiment results and analysis approve that the proposed approach is effective, improving the accuracy and reliability of fault diagnosis technology for rotating machinery vibration.
Reliability electric industry vibration signal wavelet transform fuzzy theory pattern recognition turbo-generator set
Liu Hua Li Zhanfeng Zhaowei
Hebei University of Engineering, Handan 056038
国际会议
2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)
广西桂林
英文
5492-5495
2009-06-17（万方平台首次上网日期，不代表论文的发表时间）